Enhanced Microfiber Vector Haptic Skin for Texture Angle Information Extraction

Fiber optic haptic skin is a type of wearable device that mimics the structure of the human skin's tactile sensing system. Due to its advantages such as high sensitivity and resistance to electromagnetic interference, it is gradually replacing electronic skin as a new application solution in the fields of health monitoring and human-computer interaction. However, most current fiber optic haptic skins use a single optical fiber as a point-based sensing unit, which cannot accurately perceive the vector angle information of stress when touching complex textures. Here, we explore an enhanced structure for texture angle information extraction based on a dual microfiber optical multi-axis tactile sensing unit. This structure can effectively distinguish between normal and axial forces, significantly expanding its potential applications in the field of tactile sensing. We verified the influence of the waist-crossing angle <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> on the vector information extraction performance of anisotropic sensing units through simulations and experiments. By processing the sensing signals using multiple linear regression algorithms and machine learning algorithms, we achieved vector decoupling reconstruction of force and optical power loss, with average errors of 0.3877 dB and 0.0347 dB, respectively. As a proof of concept, we tested its classification ability for the texture of leaf vein patterns using machine learning algorithms, achieving an accuracy rate of 98.5%. This work lays an important foundation for high-precision fiber optic texture tactile sensing.